def test_plot_confusion_matrix(self): cf_cv = ClassifierCv(self.labels, self.texts) name = 'MultinomialNB' metric = 'f1' cf_cv.train_save_metrics([ ('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB(alpha=.05)), ], metric, name, self.test_dir, self.test_dir) filename = os.path.join(self.test_dir, name + '_confusion_matrix.png') cf_cv.plot_confusion_matrix(savefile=filename) self.assertTrue(os.path.isfile(filename))
def test_plot_confusion_matrix_eval_data_normalize(self): cf_cv = ClassifierCv(self.labels, self.texts) name = 'MultinomialNB' metric = 'f1' cf_cv.train_save_metrics([ ('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB(alpha=.05)), ], metric, name, self.test_dir, self.test_dir) filename = os.path.join(self.test_dir, name + 'eval_confusion_matrix.png') cf_cv.labels_eval_real = ['pos', 'neg', 'neg', 'neg'] cf_cv.labels_eval_predicted = ['pos', 'neg', 'neg', 'neg'] cf_cv.plot_confusion_matrix(savefile=filename, normalize=True, use_evaluation_data=True) self.assertTrue(os.path.isfile(filename))